Search Results

NASA and the FAA conducted two flight campaigns to quantify onboard weather radar measurements with in-situ measurements of high concentrations of ice crystals found in deep convective storms. The ultimate goal of this research was to improve the understanding of high ice water content (HIWC) and develop onboard weather radar processing techniques to detect regions of HIWC ahead of an aircraft to enable tactical avoidance of the potentially hazardous conditions. Both HIWC RADAR campaigns utilized the NASA DC-8 Airborne Science Laboratory equipped with a Honeywell RDR-4000 weather radar and in-situ microphysical instruments to characterize the ice crystal clouds. The purpose of this paper is to summarize how these campaigns were conducted and highlight key results. The first campaign was conducted in August 2015 with a base of operations in Ft. Lauderdale, Florida.

The Elytron 2S is a prototype aircraft concept to allow VTOL capabilities together with fixed wing aircraft performance. It has a box wing design with a centrally mounted tilt-wing supporting two rotors. This paper explores the aerodynamic characteristics of the aircraft using computational fluid dynamics in hover and low speed forward flight, as well as analyzing the unique control system in place for hover. The results are then used to build an input set for NASA Design and Analysis if Rotorcraft software allowing trim and flight stability and control estimations to be made with SIMPLI-FLYD.

When the demand for either a region of airspace or an airport approaches or exceeds the available capacity, miles-in-trail (MIT) restrictions are the most frequently issued traffic management initiatives (TMIs) that are used to mitigate these imbalances. Miles-in-trail operations require aircraft in a traffic stream to meet a specific inter-aircraft separation in exchange for maintaining a safe and orderly flow within the stream. This stream of aircraft can be departing an airport, over a common fix, through a sector, on a specific route or arriving at an airport. This study begins by providing a high-level overview of the distribution and causes of arrival MIT restrictions for the top ten airports in the United States. This is followed by an in-depth analysis of the frequency, duration and cause of MIT restrictions impacting the Hartsfield-Jackson Atlanta International Airport (ATL) from 2009 through 2011.

This paper features a set of advanced technologies for autonomy and intelligence in advanced inspection systems of facility operations. These technologies offer a significant contribution to set a path to establish a system and an operating environment with autonomy and intelligence for inspection, monitoring and safety via gas and ambient sensors, video mining and speech recognition commands on unmanned ground vehicles and other platforms to support operational activities in the Cryogenics Test bed and other facilities and vehicles. These advanced technologies are in current development and progress and their functions and operations require guidance and formulation in conjunction with the development team(s) toward the system architecture.

This paper presents a novel health monitoring and fault adaptive control architecture for an unmanned hexrotor helicopter. The technologies developed to achieve the described level of robust fault contingency management include; 1.) A Particle Swarm Optimization (PSO) routine for maximizing the “built-in” fault tolerance that the closed loop flight control system affords, 2.) A two-stage Kalman filter scheme for real-time identification of faults that are masked by control system compensation, and 3.) A reconfigurable control allocation method which compensates for large degradations of the six main motor/rotor assemblies. The fault adaptive control system presented herein has strong robustness against small faults without the need for controller reconfiguration, and strong tolerance of large faults through adaptive accommodation of the fault source and severity.

Scheduled arriving aircraft demand may exceed airport arrival capacity when there is abnormal weather at an airport. In such situations, Federal Aviation Administration (FAA) institutes ground-delay programs (GDP) to delay flights before they depart from their originating airports. Efficient GDP planning depends on the accuracy of prediction of airport capacity and demand in the presence of uncertainties in weather forecast. This paper presents a study of the impact of dynamic airport surface weather on GDPs. Using the National Traffic Management Log, effect of weather conditions on the characteristics of GDP events at selected busy airports is investigated. Two machine learning methods are used to generate models that map the airport operational conditions and weather information to issued GDP parameters and results of validation tests are described.

The cognitive abilities of some astronauts are affected during spaceflight. We investigated whether a simulated space flight ascent environment, including vibration and 3.8 Gx ascent forces, would result in cognitive deficits detectable by the WinSCAT test battery. Eleven participants were administered the computerized cognitive test battery, a workload rating questionnaire and a subjective state questionnaire before and after a combination of acceleration plus vibration conditions. The acceleration plus vibration exposure resulted in significant self-reports of physical discomfort but did not significantly affect cognitive test battery scores. We discuss ways in which a cognitive assessment tool could be made more sensitive to subtle cognitive changes relevant to astronaut performance.

In the cockpit of NASA's next generation spacecraft, most vehicle command will be performed via electronic interfaces instead of hard cockpit switches. Checklists will be also displayed and completed on electronic procedure viewers rather than on paper. Transitioning to electronic cockpit interfaces opens up opportunities for more automated assistance, including automated root-cause diagnosis capability. The paper reports an empirical study evaluating two potential concepts for fault management interfaces incorporating two different levels of automation. The operator performance benefits produced by automation were assessed. Also, some design recommendations for spacecraft fault management interfaces are discussed.

Waste management is a vital function of spacecraft life support systems as it is necessary to meet crew health and safety and quality of life requirements. Depending on the specific mission requirements, waste management operations can include waste collection, segregation, containment, processing, storage and disposal. For the Crew Exploration Vehicle (CEV), addressing volume and mass constraints is paramount. Reducing the volume of trash prior to storage is a viable means to recover habitable volume, and is therefore a particularly desirable waste management function to implement in the CEV, and potentially in other spacecraft as well. Research is currently being performed at NASA Ames Research Center to develop waste compaction systems that can provide both volume and mass savings for the CEV and other missions.

The On-line Project Information System (OPIS) is a LAMP-based (Linux, Apache, MySQL, PHP) system being developed at NASA Ames Research Center to improve Agency information transfer and data availability, largely for improvement of system analysis and engineering. The tool will enable users to investigate NASA technology development efforts, connect with experts, and access technology development data. OPIS is currently being developed for NASA's Exploration Life Support (ELS) Project. Within OPIS, NASA ELS Managers assign projects to Principal Investigators (PI), track responsible individuals and institutions, and designate reporting assignments. Each PI populates a “Project Page” with a project overview, team member information, files, citations, and images. PI's may also delegate on-line report viewing and editing privileges to specific team members. Users can browse or search for project and member information.

Biomolecules exhibit specific binding and high affinity for their ligands. These properties can be exploited to produce sensitive, specific, real-time sensors for analytes that cannot be readily monitored by other methods. Several technologies for environmental monitoring using proteins are currently being developed. We discuss specific challenges to practical application of a family of protein-based sensors derived from bacterial periplasmic binding proteins. We also present recent work to address these challenges.

This paper describes a multi-disciplinary damage detection methodology that can aid in detecting and diagnosing a damage in a given structural system, not limited to the example of a rotating gear presented here. Damage detection is performed on the gear stress data corresponding to the steady state conditions. The normal and damage data are generated by a finite-difference solution of elastodynamic equations of velocity and stress in generalized coordinates1. The elastodynamic solution provides a knowledge of the stress distribution over the gear such as locations of stress extrema, which in turn can lead to an optimal placement of appropriate sensors over the gear to detect a potential damage. The damage detection is performed by a multi-function optimization that incorporates Tikhonov kernel regularization reinforced by an added Laplacian regularization term as used in semi-supervised machine learning. Damage is mimicked by reducing the rigidity of one of the gear teeth.

This paper discussed how data mining techniques could give advantage to the robot in navigation, in terms of speed. The input of our navigation system is the sensory information collected by the robot's equipped landmark sensor and infra-red sensor, the process of the system is the proposed data mining technique, and the output of the system is the execution of the moving direction in a 2D Martian environment. The results demonstrate efficient goal-oriented navigation using data mining techniques.

This paper describes the initial results of applying two machine-learning-based unsupervised anomaly detection algorithms, Orca and GritBot, to data from two rocket propulsion testbeds. The first testbed uses historical data from the Space Shuttle Main Engine. The second testbed uses data from an experimental rocket engine test stand located at NASA Stennis Space Center. The paper describes four candidate anomalies detected by the two algorithms.

This paper considers system design and technology selection for the crew air and water recycling systems to be used in long duration human space exploration. The ultimate objective is to identify the air and water technologies likely to be used for the vision for space exploration and to suggest alternate technologies that should be developed. The approach is to conduct a preliminary systems engineering analysis, beginning with the Air and Water System (AWS) requirements and the system mass balance, and then to define the functional architecture, review the current International Space Station (ISS) technologies, and suggest alternate technologies.

Pyrolysis is a very versatile waste processing technology which can be tailored to produce a variety of solid liquid and/or gaseous products. The main disadvantages of pyrolysis processing are: (1) the product stream is more complex than for many of the alternative treatments; (2) the product gases cannot be vented directly into the cabin without further treatment because of the high CO concentrations. One possible solution is to combine a pyrolysis step with catalytic oxidation (combustion) of the effluent gases. This integration takes advantage of the best features of each process, which is insensitivity to product mix, no O2 consumption, and batch processing, in the case of pyrolysis, and simplicity of the product effluent stream in the case of oxidation. In addition, this hybrid process has the potential to result in a significant reduction in Equivalent System Mass (ESM) and system complexity.

One of the major problems associated with solid waste processing technologies is effluent contaminants that are released in gaseous forms from the processes. This is a concern in both biological as well as physicochemical solid waste processing. Carbon dioxide (CO2), the major gas released, does not present a serious problem and there are currently in place a number of flight-qualified technologies for CO2 removal. However, a number of other gases, in particular NOx, SO2, NH3, and various hydrocarbons (e.g. CH4) do present health hazards to the crew members in space habitats. In the present configuration of solid waste processing in the International Space Station (ISS), some of these gases are removed by the Trace Contaminant Control System (TCCS), demands a major resupply. Reduction of the resupply can be effective by using catalyst impregnated carbon nanotubes. For example, NO decomposition to N2 and O2 is thermodynamically favored.

The paper describes an approach to the integration of qualitative and quantitative modeling techniques for advanced life support (ALS) systems. Developing reliable control strategies that scale up to fully integrated life support systems requires augmenting quantitative models and control algorithms with the abstractions provided by qualitative, symbolic models and their associated high-level control strategies. This will allow for effective management of the combinatorics due to the integration of a large number of ALS subsystems. By focusing control actions at different levels of detail and reactivity we can use faster, simpler responses at the lowest level and predictive but complex responses at the higher levels of abstraction. In particular, methods from model-based planning and scheduling can provide effective resource management over long time periods.

This paper describes recent work on developing an extensible information grid for risk management at NASA — a RISK INFORMATION GRID. This grid is being developed by integrating information grid technology with risk management processes for a variety of risk related applications. To date, RISK GRID applications are being developed for three main NASA processes: risk management — a closed-loop iterative process for explicit risk management, program/project management — a proactive process that includes risk management, and mishap management — a feedback loop for learning from historical risks that ‘escaped’ other processes. This is enabled through an architecture involving an extensible database, structuring information with XML, ‘schema-less’ mapping of XML, and secure server-mediated communication using standard protocols.

Design, operations and maintenance activities in aviation involve analysis of variety of aviation data. This data is typically in disparate formats making it difficult to use with different software packages. Use of a self-describing and extensible standard called XML provides a solution to this interoperability problem. While self-describing nature of XML makes it easy to reuse, it also increases the size of data significantly. A natural solution to the problem is to compress the data using suitable algorithm and transfer it in the compressed form. We found that XML-specific compressors such as Xmill and XMLPPM generally outperform traditional compressors. However, optimal use of Xmill requires of discovery of optimal options to use while running Xmill. Manual discovery of optimal setting can require an engineer to experiment for weeks.